On the open-source landscape of PLOS Computational Biology

This is the authors' abstract. We don't add key points for this paper.

Over the past year, I (M.B.) have been investigating the landscape of code-sharing in academic journals across different research fields.At the end of my PhD, I made the choice to share code that reproduces figures from one of my papers [1], and since then, I've been involved in several open-source projects (qMRLab and AxonDeepSeg) and initiatives dealing with open science in publishing (NeuroLibre and Canadian Open Neuroscience Platform).Recently, following an editorial by N.S. on reproducibility and the future of MRI research [2], we wrote a blog post presenting an analysis of the open-source landscape for the journal Magnetic Resonance in Medicine (MRM), which broadly focuses on MRI research for medical applications.These findings provided a snapshot of the current state of the open-source landscape for that journal (e.g., most used coding language is still MATLAB) and some insights into new trends (12% of the articles shared code that reproduced figures).In this editorial, we examine the open-source landscape of PLOS Computational Biology.PLOS Computational Biology is inherently different from MRM not only because of the difference in research topics, but also because it's an openaccess journal that focuses primarily on computational studies.The broad questions that were of interest are the following:• What percentage of PLOS Computational Biology publications claim to share code?• If they share code, what coding languages do they use?• Where do the authors typically host their code?• How many publications share scripts that reproduce some or all of the figures from their paper? Details of analysisTo perform this analysis, all the articles published in PLOS Computational Biology from January to December 2019 were downloaded.A script was then executed to search for all the articles that contained one of a list of keywords that may hint at containing code/data.Following that, all the articles that matched keywords were compiled into a Google Sheet file and manually searched inside each of those articles to determine if the code they used was actually shared.The external links in the articles were then examined to see if they (1) shared code; (2) see which languages the code used;(3) where they hosted their code; and (4) if the code aimed to reproduce any of the figures.See Table 1 for an overview of results.Overall, 41% of the articles published in PLOS Computational Biology reported sharing some code.It is possible that the rate is even slightly higher, as some articles that reported